Network dependence testing via diffusion maps and distance-based correlations
Abstract
Deciphering the associations between network connectivity and nodal attributes is one of the core problems in network science. The dependency structure and high dimensionality of networks pose unique challenges to traditional dependency tests in terms of theoretical guarantees and empirical performance. We propose an approach to test network dependence via diffusion maps and distance-based correlations. We prove that the new method yields a consistent test statistic under mild distributional assumptions on the graph structure, and demonstrate that it is able to efficiently identify the most informative graph embedding with respect to the diffusion time. The methodology is illustrated on both simulated and real data.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Sep 30, 2019
- Source ID
- 10.1093/biomet/asz045
Entities
People
- Carey E. Priebe
- Cencheng Shen
- Joshua T Vogelstein
- Youjin Lee
Organizations
- Defense Advanced Research Projects Agency
- Johns Hopkins University
- National Science Foundation
- University of Delaware
- University of Pennsylvania